Overview

Dataset statistics

Number of variables26
Number of observations52610
Missing cells24815
Missing cells (%)1.8%
Duplicate rows4946
Duplicate rows (%)9.4%
Total size in memory10.5 MiB
Average record size in memory209.0 B

Variable types

Numeric19
Categorical5
Boolean2

Alerts

Dataset has 4946 (9.4%) duplicate rowsDuplicates
headCoach has a high cardinality: 146 distinct values High cardinality
gameID is highly correlated with shots and 6 other fieldsHigh correlation
pim is highly correlated with timeOnIce and 2 other fieldsHigh correlation
powerPlayOpportunities is highly correlated with evenTimeOnIce and 1 other fieldsHigh correlation
shots is highly correlated with gameID and 6 other fieldsHigh correlation
takeaways is highly correlated with gameID and 6 other fieldsHigh correlation
hits is highly correlated with gameID and 6 other fieldsHigh correlation
blockedShots is highly correlated with gameID and 6 other fieldsHigh correlation
giveaways is highly correlated with gameID and 6 other fieldsHigh correlation
missedShots is highly correlated with gameID and 6 other fieldsHigh correlation
wonFaceoffs is highly correlated with gameID and 6 other fieldsHigh correlation
timeOnIce is highly correlated with pim and 2 other fieldsHigh correlation
evenTimeOnIce is highly correlated with pim and 4 other fieldsHigh correlation
shortHandedTimeOnIce is highly correlated with pim and 2 other fieldsHigh correlation
powerPlayTimeOnIce is highly correlated with powerPlayOpportunities and 1 other fieldsHigh correlation
gameID is highly correlated with shots and 6 other fieldsHigh correlation
pim is highly correlated with shortHandedTimeOnIceHigh correlation
powerPlayOpportunities is highly correlated with evenTimeOnIce and 1 other fieldsHigh correlation
shots is highly correlated with gameID and 6 other fieldsHigh correlation
takeaways is highly correlated with gameID and 6 other fieldsHigh correlation
hits is highly correlated with gameID and 6 other fieldsHigh correlation
blockedShots is highly correlated with gameID and 6 other fieldsHigh correlation
giveaways is highly correlated with gameID and 6 other fieldsHigh correlation
missedShots is highly correlated with gameID and 6 other fieldsHigh correlation
wonFaceoffs is highly correlated with gameID and 6 other fieldsHigh correlation
timeOnIce is highly correlated with evenTimeOnIceHigh correlation
evenTimeOnIce is highly correlated with powerPlayOpportunities and 3 other fieldsHigh correlation
shortHandedTimeOnIce is highly correlated with pim and 1 other fieldsHigh correlation
powerPlayTimeOnIce is highly correlated with powerPlayOpportunities and 1 other fieldsHigh correlation
gameID is highly correlated with shots and 6 other fieldsHigh correlation
pim is highly correlated with shortHandedTimeOnIceHigh correlation
powerPlayOpportunities is highly correlated with evenTimeOnIce and 1 other fieldsHigh correlation
shots is highly correlated with gameID and 6 other fieldsHigh correlation
takeaways is highly correlated with gameID and 6 other fieldsHigh correlation
hits is highly correlated with gameID and 6 other fieldsHigh correlation
blockedShots is highly correlated with gameID and 6 other fieldsHigh correlation
giveaways is highly correlated with gameID and 6 other fieldsHigh correlation
missedShots is highly correlated with gameID and 6 other fieldsHigh correlation
wonFaceoffs is highly correlated with gameID and 6 other fieldsHigh correlation
evenTimeOnIce is highly correlated with powerPlayOpportunities and 2 other fieldsHigh correlation
shortHandedTimeOnIce is highly correlated with pim and 1 other fieldsHigh correlation
powerPlayTimeOnIce is highly correlated with powerPlayOpportunities and 1 other fieldsHigh correlation
gameID is highly correlated with shots and 6 other fieldsHigh correlation
settledIn is highly correlated with faceOffWinPercentage and 2 other fieldsHigh correlation
powerPlayOpportunities is highly correlated with powerPlayGoals and 2 other fieldsHigh correlation
powerPlayGoals is highly correlated with powerPlayOpportunitiesHigh correlation
faceOffWinPercentage is highly correlated with settledInHigh correlation
startRinkSide is highly correlated with TeamNameHigh correlation
shots is highly correlated with gameID and 6 other fieldsHigh correlation
takeaways is highly correlated with gameID and 4 other fieldsHigh correlation
hits is highly correlated with gameID and 6 other fieldsHigh correlation
blockedShots is highly correlated with gameID and 5 other fieldsHigh correlation
giveaways is highly correlated with gameID and 6 other fieldsHigh correlation
missedShots is highly correlated with gameID and 5 other fieldsHigh correlation
wonFaceoffs is highly correlated with gameID and 6 other fieldsHigh correlation
teamID is highly correlated with settledIn and 2 other fieldsHigh correlation
timeOnIce is highly correlated with settledIn and 2 other fieldsHigh correlation
evenTimeOnIce is highly correlated with powerPlayOpportunities and 3 other fieldsHigh correlation
shortHandedTimeOnIce is highly correlated with evenTimeOnIceHigh correlation
powerPlayTimeOnIce is highly correlated with powerPlayOpportunities and 1 other fieldsHigh correlation
TeamName is highly correlated with startRinkSide and 1 other fieldsHigh correlation
faceOffWinPercentage has 22148 (42.1%) missing values Missing
startRinkSide has 2392 (4.5%) missing values Missing
hoa is uniformly distributed Uniform
powerPlayOpportunities has 750 (1.4%) zeros Zeros
powerPlayGoals has 26842 (51.0%) zeros Zeros
shots has 22552 (42.9%) zeros Zeros
goals has 23590 (44.8%) zeros Zeros
takeaways has 26046 (49.5%) zeros Zeros
hits has 22596 (43.0%) zeros Zeros
blockedShots has 23030 (43.8%) zeros Zeros
giveaways has 24876 (47.3%) zeros Zeros
missedShots has 23543 (44.8%) zeros Zeros
penalties has 11303 (21.5%) zeros Zeros
wonFaceoffs has 22548 (42.9%) zeros Zeros
shortHandedTimeOnIce has 751 (1.4%) zeros Zeros
powerPlayTimeOnIce has 752 (1.4%) zeros Zeros

Reproduction

Analysis started2022-02-27 02:36:59.246011
Analysis finished2022-02-27 02:37:52.417921
Duration53.17 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

gameID
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct23735
Distinct (%)45.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010765072
Minimum2000020001
Maximum2019040653
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size822.0 KiB
2022-02-26T21:37:52.516308image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2000020001
5-th percentile2001020086
Q12006020427
median2011020764
Q32016030151
95-th percentile2019020555
Maximum2019040653
Range19020652
Interquartile range (IQR)10009724

Descriptive statistics

Standard deviation6073510.283
Coefficient of variation (CV)0.003020497206
Kurtosis-1.21436123
Mean2010765072
Median Absolute Deviation (MAD)5000414
Skewness-0.2417994037
Sum1.057863505 × 1014
Variance3.688752716 × 1013
MonotonicityNot monotonic
2022-02-26T21:37:52.755960image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20190202214
 
< 0.1%
20190209514
 
< 0.1%
20190201944
 
< 0.1%
20180201334
 
< 0.1%
20180302234
 
< 0.1%
20190204774
 
< 0.1%
20190209834
 
< 0.1%
20180207464
 
< 0.1%
20180210774
 
< 0.1%
20190201624
 
< 0.1%
Other values (23725)52570
99.9%
ValueCountFrequency (%)
20000200012
< 0.1%
20000200022
< 0.1%
20000200032
< 0.1%
20000200042
< 0.1%
20000200052
< 0.1%
20000200062
< 0.1%
20000200072
< 0.1%
20000200082
< 0.1%
20000200092
< 0.1%
20000200102
< 0.1%
ValueCountFrequency (%)
20190406534
< 0.1%
20190406524
< 0.1%
20190406514
< 0.1%
20190304164
< 0.1%
20190304154
< 0.1%
20190304144
< 0.1%
20190304134
< 0.1%
20190304124
< 0.1%
20190304114
< 0.1%
20190303254
< 0.1%

hoa
Categorical

UNIFORM

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size822.0 KiB
home
26305 
away
26305 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowaway
2nd rowhome
3rd rowaway
4th rowhome
5th rowaway

Common Values

ValueCountFrequency (%)
home26305
50.0%
away26305
50.0%

Length

2022-02-26T21:37:52.873088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-26T21:37:52.932539image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
home26305
50.0%
away26305
50.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

won
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size462.4 KiB
False
26947 
True
25663 
ValueCountFrequency (%)
False26947
51.2%
True25663
48.8%
2022-02-26T21:37:52.969596image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

settledIn
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size822.0 KiB
REG
40306 
OT
12256 
tbc
 
48

Length

Max length3
Median length3
Mean length2.767040487
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowREG
2nd rowREG
3rd rowOT
4th rowOT
5th rowREG

Common Values

ValueCountFrequency (%)
REG40306
76.6%
OT12256
 
23.3%
tbc48
 
0.1%

Length

2022-02-26T21:37:53.044684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-26T21:37:53.112556image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
reg40306
76.6%
ot12256
 
23.3%
tbc48
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

headCoach
Categorical

HIGH CARDINALITY

Distinct146
Distinct (%)0.3%
Missing28
Missing (%)0.1%
Memory size822.0 KiB
Barry Trotz
 
1804
Joel Quenneville
 
1599
John Tortorella
 
1563
Claude Julien
 
1500
Mike Babcock
 
1475
Other values (141)
44641 

Length

Max length20
Median length12
Mean length12.45519379
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowDave Hakstol
2nd rowJoel Quenneville
3rd rowRandy Carlyle
4th rowPhil Housley
5th rowPatrick Roy

Common Values

ValueCountFrequency (%)
Barry Trotz1804
 
3.4%
Joel Quenneville1599
 
3.0%
John Tortorella1563
 
3.0%
Claude Julien1500
 
2.9%
Mike Babcock1475
 
2.8%
Peter Laviolette1422
 
2.7%
Paul Maurice1397
 
2.7%
Ken Hitchcock1336
 
2.5%
Dave Tippett1283
 
2.4%
Lindy Ruff1273
 
2.4%
Other values (136)37930
72.1%

Length

2022-02-26T21:37:53.194053image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mike3421
 
3.3%
john2800
 
2.7%
peter2688
 
2.6%
dave2196
 
2.1%
todd1834
 
1.7%
barry1820
 
1.7%
trotz1804
 
1.7%
bruce1804
 
1.7%
claude1701
 
1.6%
paul1653
 
1.6%
Other values (212)83452
79.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

pim
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct110
Distinct (%)0.2%
Missing8
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3.918254629
Minimum0
Maximum71
Zeros436
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size822.0 KiB
2022-02-26T21:37:53.306057image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.67
Q12
median3.33
Q35
95-th percentile9.33
Maximum71
Range71
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.009885919
Coefficient of variation (CV)0.7681700666
Kurtosis31.88900286
Mean3.918254629
Median Absolute Deviation (MAD)1.33
Skewness3.537117641
Sum206108.03
Variance9.059413248
MonotonicityNot monotonic
2022-02-26T21:37:53.429289image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27412
14.1%
2.676975
13.3%
1.335615
10.7%
3.335228
 
9.9%
43401
 
6.5%
0.672477
 
4.7%
4.672235
 
4.2%
4.331933
 
3.7%
3.671786
 
3.4%
51687
 
3.2%
Other values (100)13853
26.3%
ValueCountFrequency (%)
0436
 
0.8%
0.672477
 
4.7%
1.335615
10.7%
1.6767
 
0.1%
27412
14.1%
2.33547
 
1.0%
2.676975
13.3%
31201
 
2.3%
3.335228
9.9%
3.671786
 
3.4%
ValueCountFrequency (%)
711
< 0.1%
68.671
< 0.1%
67.331
< 0.1%
611
< 0.1%
54.331
< 0.1%
45.331
< 0.1%
432
< 0.1%
42.671
< 0.1%
41.671
< 0.1%
38.671
< 0.1%

powerPlayOpportunities
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct17
Distinct (%)< 0.1%
Missing8
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.231235124
Minimum0
Maximum5.33
Zeros750
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size822.0 KiB
2022-02-26T21:37:53.542879image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.33
Q10.67
median1
Q31.67
95-th percentile2.33
Maximum5.33
Range5.33
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.623485455
Coefficient of variation (CV)0.506390244
Kurtosis0.918459439
Mean1.231235124
Median Absolute Deviation (MAD)0.33
Skewness0.7311674479
Sum64765.43
Variance0.3887341125
MonotonicityNot monotonic
2022-02-26T21:37:53.640284image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
111932
22.7%
1.3310518
20.0%
0.679577
18.2%
1.677240
13.8%
0.334341
 
8.3%
24233
 
8.0%
2.332148
 
4.1%
2.671045
 
2.0%
0750
 
1.4%
3479
 
0.9%
Other values (7)339
 
0.6%
ValueCountFrequency (%)
0750
 
1.4%
0.334341
 
8.3%
0.679577
18.2%
111932
22.7%
1.3310518
20.0%
1.677240
13.8%
24233
 
8.0%
2.332148
 
4.1%
2.671045
 
2.0%
3479
 
0.9%
ValueCountFrequency (%)
5.331
 
< 0.1%
52
 
< 0.1%
4.674
 
< 0.1%
4.3318
 
< 0.1%
429
 
0.1%
3.6787
 
0.2%
3.33198
 
0.4%
3479
 
0.9%
2.671045
2.0%
2.332148
4.1%

powerPlayGoals
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)< 0.1%
Missing8
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.2217744572
Minimum0
Maximum2.33
Zeros26842
Zeros (%)51.0%
Negative0
Negative (%)0.0%
Memory size822.0 KiB
2022-02-26T21:37:53.725947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.33
95-th percentile0.67
Maximum2.33
Range2.33
Interquartile range (IQR)0.33

Descriptive statistics

Standard deviation0.2711537821
Coefficient of variation (CV)1.2226556
Kurtosis1.692366185
Mean0.2217744572
Median Absolute Deviation (MAD)0
Skewness1.249086739
Sum11665.78
Variance0.07352437353
MonotonicityNot monotonic
2022-02-26T21:37:53.809935image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
026842
51.0%
0.3318223
34.6%
0.676017
 
11.4%
11271
 
2.4%
1.33201
 
0.4%
1.6742
 
0.1%
25
 
< 0.1%
2.331
 
< 0.1%
(Missing)8
 
< 0.1%
ValueCountFrequency (%)
026842
51.0%
0.3318223
34.6%
0.676017
 
11.4%
11271
 
2.4%
1.33201
 
0.4%
1.6742
 
0.1%
25
 
< 0.1%
2.331
 
< 0.1%
ValueCountFrequency (%)
2.331
 
< 0.1%
25
 
< 0.1%
1.6742
 
0.1%
1.33201
 
0.4%
11271
 
2.4%
0.676017
 
11.4%
0.3318223
34.6%
026842
51.0%

faceOffWinPercentage
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct428
Distinct (%)1.4%
Missing22148
Missing (%)42.1%
Infinite0
Infinite (%)0.0%
Mean49.96717878
Minimum0
Maximum79.2
Zeros20
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size822.0 KiB
2022-02-26T21:37:53.917025image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile38.1
Q145.2
median50
Q354.8
95-th percentile61.9
Maximum79.2
Range79.2
Interquartile range (IQR)9.6

Descriptive statistics

Standard deviation7.326322161
Coefficient of variation (CV)0.1466226899
Kurtosis1.250620745
Mean49.96717878
Median Absolute Deviation (MAD)4.8
Skewness-0.1952740642
Sum1522100.2
Variance53.6749964
MonotonicityNot monotonic
2022-02-26T21:37:54.044735image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
501512
 
2.9%
50.8489
 
0.9%
49.2489
 
0.9%
50.9381
 
0.7%
49.1381
 
0.7%
52.5305
 
0.6%
47.5305
 
0.6%
42.9261
 
0.5%
51.9261
 
0.5%
57.1261
 
0.5%
Other values (418)25817
49.1%
(Missing)22148
42.1%
ValueCountFrequency (%)
020
< 0.1%
20.81
 
< 0.1%
21.81
 
< 0.1%
23.11
 
< 0.1%
23.61
 
< 0.1%
23.82
 
< 0.1%
24.12
 
< 0.1%
24.41
 
< 0.1%
251
 
< 0.1%
25.95
 
< 0.1%
ValueCountFrequency (%)
79.21
 
< 0.1%
78.21
 
< 0.1%
76.91
 
< 0.1%
76.41
 
< 0.1%
76.22
 
< 0.1%
75.92
 
< 0.1%
75.61
 
< 0.1%
751
 
< 0.1%
74.15
< 0.1%
73.81
 
< 0.1%

startRinkSide
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing2392
Missing (%)4.5%
Memory size822.0 KiB
left
26852 
right
23366 

Length

Max length5
Median length4
Mean length4.46529133
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowleft
2nd rowleft
3rd rowright
4th rowright
5th rowleft

Common Values

ValueCountFrequency (%)
left26852
51.0%
right23366
44.4%
(Missing)2392
 
4.5%

Length

2022-02-26T21:37:54.170912image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-26T21:37:54.232581image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
left26852
53.5%
right23366
46.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

shots
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct37
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.018627637
Minimum0
Maximum54
Zeros22552
Zeros (%)42.9%
Negative0
Negative (%)0.0%
Memory size822.0 KiB
2022-02-26T21:37:54.302734image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q312
95-th percentile22
Maximum54
Range54
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.910036733
Coefficient of variation (CV)1.127006182
Kurtosis0.8286690795
Mean7.018627637
Median Absolute Deviation (MAD)6
Skewness1.095537627
Sum369250
Variance62.56868112
MonotonicityNot monotonic
2022-02-26T21:37:54.415423image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
022552
42.9%
82796
 
5.3%
102692
 
5.1%
122269
 
4.3%
92259
 
4.3%
72195
 
4.2%
61986
 
3.8%
141748
 
3.3%
111744
 
3.3%
161566
 
3.0%
Other values (27)10803
20.5%
ValueCountFrequency (%)
022552
42.9%
139
 
0.1%
2214
 
0.4%
3496
 
0.9%
4937
 
1.8%
51434
 
2.7%
61986
 
3.8%
72195
 
4.2%
82796
 
5.3%
92259
 
4.3%
ValueCountFrequency (%)
542
 
< 0.1%
444
 
< 0.1%
4224
 
< 0.1%
4022
 
< 0.1%
3828
 
0.1%
3670
 
0.1%
34114
 
0.2%
32200
0.4%
30332
0.6%
28434
0.8%

goals
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.971963505
Minimum0
Maximum12
Zeros23590
Zeros (%)44.8%
Negative0
Negative (%)0.0%
Memory size822.0 KiB
2022-02-26T21:37:54.510707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum12
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.198563123
Coefficient of variation (CV)1.233135932
Kurtosis5.301774179
Mean0.971963505
Median Absolute Deviation (MAD)1
Skewness1.823394651
Sum51135
Variance1.436553559
MonotonicityNot monotonic
2022-02-26T21:37:54.614151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
023590
44.8%
115066
28.6%
29812
18.7%
41957
 
3.7%
31569
 
3.0%
6458
 
0.9%
886
 
0.2%
550
 
0.1%
1020
 
< 0.1%
122
 
< 0.1%
ValueCountFrequency (%)
023590
44.8%
115066
28.6%
29812
18.7%
31569
 
3.0%
41957
 
3.7%
550
 
0.1%
6458
 
0.9%
886
 
0.2%
1020
 
< 0.1%
122
 
< 0.1%
ValueCountFrequency (%)
122
 
< 0.1%
1020
 
< 0.1%
886
 
0.2%
6458
 
0.9%
550
 
0.1%
41957
 
3.7%
31569
 
3.0%
29812
18.7%
115066
28.6%
023590
44.8%

takeaways
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.899106634
Minimum0
Maximum26
Zeros26046
Zeros (%)49.5%
Negative0
Negative (%)0.0%
Memory size822.0 KiB
2022-02-26T21:37:54.709319image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile8
Maximum26
Range26
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.740544972
Coefficient of variation (CV)1.443070612
Kurtosis5.650357601
Mean1.899106634
Median Absolute Deviation (MAD)1
Skewness2.057171326
Sum99912
Variance7.510586744
MonotonicityNot monotonic
2022-02-26T21:37:54.925414image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
026046
49.5%
26763
 
12.9%
14623
 
8.8%
44557
 
8.7%
33665
 
7.0%
62456
 
4.7%
81505
 
2.9%
51218
 
2.3%
10751
 
1.4%
12355
 
0.7%
Other values (10)671
 
1.3%
ValueCountFrequency (%)
026046
49.5%
14623
 
8.8%
26763
 
12.9%
33665
 
7.0%
44557
 
8.7%
51218
 
2.3%
62456
 
4.7%
7239
 
0.5%
81505
 
2.9%
958
 
0.1%
ValueCountFrequency (%)
264
 
< 0.1%
242
 
< 0.1%
2210
 
< 0.1%
2020
 
< 0.1%
1824
 
< 0.1%
1674
 
0.1%
14227
 
0.4%
12355
0.7%
1113
 
< 0.1%
10751
1.4%

hits
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct45
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.911784832
Minimum0
Maximum60
Zeros22596
Zeros (%)43.0%
Negative0
Negative (%)0.0%
Memory size822.0 KiB
2022-02-26T21:37:55.040898image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q312
95-th percentile22
Maximum60
Range60
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.006231764
Coefficient of variation (CV)1.158345053
Kurtosis1.88808388
Mean6.911784832
Median Absolute Deviation (MAD)5
Skewness1.286426604
Sum363629
Variance64.09974707
MonotonicityNot monotonic
2022-02-26T21:37:55.153567image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
022596
43.0%
102717
 
5.2%
82474
 
4.7%
122254
 
4.3%
62204
 
4.2%
72083
 
4.0%
91930
 
3.7%
141819
 
3.5%
111618
 
3.1%
161441
 
2.7%
Other values (35)11474
21.8%
ValueCountFrequency (%)
022596
43.0%
1113
 
0.2%
2374
 
0.7%
3643
 
1.2%
41247
 
2.4%
51415
 
2.7%
62204
 
4.2%
72083
 
4.0%
82474
 
4.7%
91930
 
3.7%
ValueCountFrequency (%)
602
 
< 0.1%
542
 
< 0.1%
526
 
< 0.1%
5018
 
< 0.1%
4814
 
< 0.1%
4622
 
< 0.1%
4418
 
< 0.1%
4226
 
< 0.1%
4064
0.1%
3866
0.1%

blockedShots
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.658810112
Minimum0
Maximum34
Zeros23030
Zeros (%)43.8%
Negative0
Negative (%)0.0%
Memory size822.0 KiB
2022-02-26T21:37:55.265540image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q36
95-th percentile12
Maximum34
Range34
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.461539261
Coefficient of variation (CV)1.219396231
Kurtosis2.161390782
Mean3.658810112
Median Absolute Deviation (MAD)2
Skewness1.416307721
Sum192490
Variance19.90533258
MonotonicityNot monotonic
2022-02-26T21:37:55.365510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
023030
43.8%
44325
 
8.2%
64039
 
7.7%
33014
 
5.7%
53005
 
5.7%
82945
 
5.6%
22716
 
5.2%
101957
 
3.7%
71819
 
3.5%
121413
 
2.7%
Other values (16)4347
 
8.3%
ValueCountFrequency (%)
023030
43.8%
11077
 
2.0%
22716
 
5.2%
33014
 
5.7%
44325
 
8.2%
53005
 
5.7%
64039
 
7.7%
71819
 
3.5%
82945
 
5.6%
9713
 
1.4%
ValueCountFrequency (%)
342
 
< 0.1%
2814
 
< 0.1%
2636
 
0.1%
2444
 
0.1%
22108
 
0.2%
211
 
< 0.1%
20206
 
0.4%
18336
0.6%
173
 
< 0.1%
16678
1.3%

giveaways
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.593898498
Minimum0
Maximum30
Zeros24876
Zeros (%)47.3%
Negative0
Negative (%)0.0%
Memory size822.0 KiB
2022-02-26T21:37:55.472264image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile10
Maximum30
Range30
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.661681059
Coefficient of variation (CV)1.411651636
Kurtosis4.480020117
Mean2.593898498
Median Absolute Deviation (MAD)1
Skewness1.929465477
Sum136465
Variance13.40790818
MonotonicityNot monotonic
2022-02-26T21:37:55.571090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
024876
47.3%
25085
 
9.7%
44447
 
8.5%
33546
 
6.7%
13374
 
6.4%
63019
 
5.7%
82000
 
3.8%
51900
 
3.6%
101335
 
2.5%
12855
 
1.6%
Other values (15)2173
 
4.1%
ValueCountFrequency (%)
024876
47.3%
13374
 
6.4%
25085
 
9.7%
33546
 
6.7%
44447
 
8.5%
51900
 
3.6%
63019
 
5.7%
7686
 
1.3%
82000
 
3.8%
9180
 
0.3%
ValueCountFrequency (%)
302
 
< 0.1%
286
 
< 0.1%
266
 
< 0.1%
2416
 
< 0.1%
2262
 
0.1%
20106
 
0.2%
18146
0.3%
171
 
< 0.1%
16314
0.6%
153
 
< 0.1%

missedShots
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.899809922
Minimum0
Maximum34
Zeros23543
Zeros (%)44.8%
Negative0
Negative (%)0.0%
Memory size822.0 KiB
2022-02-26T21:37:55.674435image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35
95-th percentile10
Maximum34
Range34
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.674667594
Coefficient of variation (CV)1.267209815
Kurtosis3.125157343
Mean2.899809922
Median Absolute Deviation (MAD)2
Skewness1.588646533
Sum152559
Variance13.50318193
MonotonicityNot monotonic
2022-02-26T21:37:55.779667image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
023543
44.8%
45112
 
9.7%
24416
 
8.4%
33866
 
7.3%
63690
 
7.0%
52700
 
5.1%
82413
 
4.6%
11913
 
3.6%
101545
 
2.9%
71029
 
2.0%
Other values (15)2383
 
4.5%
ValueCountFrequency (%)
023543
44.8%
11913
 
3.6%
24416
 
8.4%
33866
 
7.3%
45112
 
9.7%
52700
 
5.1%
63690
 
7.0%
71029
 
2.0%
82413
 
4.6%
9260
 
0.5%
ValueCountFrequency (%)
342
 
< 0.1%
322
 
< 0.1%
302
 
< 0.1%
264
 
< 0.1%
2410
 
< 0.1%
2244
 
0.1%
2066
 
0.1%
18152
0.3%
16292
0.6%
152
 
< 0.1%

penalties
Real number (ℝ≥0)

ZEROS

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.742197301
Minimum0
Maximum16
Zeros11303
Zeros (%)21.5%
Negative0
Negative (%)0.0%
Memory size822.0 KiB
2022-02-26T21:37:55.883615image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile4
Maximum16
Range16
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.49272232
Coefficient of variation (CV)0.8568044039
Kurtosis3.217920507
Mean1.742197301
Median Absolute Deviation (MAD)1
Skewness1.268606293
Sum91657
Variance2.228219924
MonotonicityNot monotonic
2022-02-26T21:37:55.986038image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
215247
29.0%
113976
26.6%
011303
21.5%
35494
 
10.4%
44441
 
8.4%
6934
 
1.8%
5817
 
1.6%
8204
 
0.4%
7119
 
0.2%
1032
 
0.1%
Other values (7)43
 
0.1%
ValueCountFrequency (%)
011303
21.5%
113976
26.6%
215247
29.0%
35494
 
10.4%
44441
 
8.4%
5817
 
1.6%
6934
 
1.8%
7119
 
0.2%
8204
 
0.4%
924
 
< 0.1%
ValueCountFrequency (%)
163
 
< 0.1%
151
 
< 0.1%
144
 
< 0.1%
131
 
< 0.1%
129
 
< 0.1%
111
 
< 0.1%
1032
 
0.1%
924
 
< 0.1%
8204
0.4%
7119
0.2%

wonFaceoffs
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct33
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.335069378
Minimum0
Maximum42
Zeros22548
Zeros (%)42.9%
Negative0
Negative (%)0.0%
Memory size822.0 KiB
2022-02-26T21:37:56.103382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7
Q312
95-th percentile22
Maximum42
Range42
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.853894246
Coefficient of variation (CV)1.070732101
Kurtosis0.1055568001
Mean7.335069378
Median Absolute Deviation (MAD)7
Skewness0.8606341418
Sum385898
Variance61.68365483
MonotonicityNot monotonic
2022-02-26T21:37:56.207193image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
022548
42.9%
103010
 
5.7%
82619
 
5.0%
92618
 
5.0%
122431
 
4.6%
112211
 
4.2%
141971
 
3.7%
71952
 
3.7%
161657
 
3.1%
181524
 
2.9%
Other values (23)10069
19.1%
ValueCountFrequency (%)
022548
42.9%
16
 
< 0.1%
254
 
0.1%
3165
 
0.3%
4442
 
0.8%
5892
 
1.7%
61500
 
2.9%
71952
 
3.7%
82619
 
5.0%
92618
 
5.0%
ValueCountFrequency (%)
424
 
< 0.1%
4010
 
< 0.1%
3818
 
< 0.1%
3638
 
0.1%
3492
 
0.2%
32122
 
0.2%
30242
 
0.5%
28360
0.7%
26662
1.3%
24896
1.7%

teamID
Real number (ℝ≥0)

HIGH CORRELATION

Distinct37
Distinct (%)0.1%
Missing28
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean16.87486212
Minimum1
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size822.0 KiB
2022-02-26T21:37:56.321235image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q324
95-th percentile30
Maximum90
Range89
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.18810042
Coefficient of variation (CV)0.6630039608
Kurtosis2.360431562
Mean16.87486212
Median Absolute Deviation (MAD)8
Skewness1.129943485
Sum887314
Variance125.1735911
MonotonicityNot monotonic
2022-02-26T21:37:56.429162image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
61822
 
3.5%
141804
 
3.4%
191794
 
3.4%
281785
 
3.4%
151785
 
3.4%
51784
 
3.4%
161770
 
3.4%
31767
 
3.4%
251761
 
3.3%
181759
 
3.3%
Other values (27)34751
66.1%
ValueCountFrequency (%)
11691
3.2%
21743
3.3%
31767
3.4%
41735
3.3%
51784
3.4%
61822
3.5%
71669
3.2%
81732
3.3%
91708
3.2%
101708
3.2%
ValueCountFrequency (%)
904
 
< 0.1%
894
 
< 0.1%
886
 
< 0.1%
876
 
< 0.1%
54462
 
0.9%
53650
 
1.2%
52886
1.7%
301714
3.3%
291721
3.3%
281785
3.4%

timeOnIce
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3088
Distinct (%)5.9%
Missing28
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean329.5376174
Minimum133.33
Maximum827.26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size822.0 KiB
2022-02-26T21:37:56.548335image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum133.33
5-th percentile317.76
Q1323.91
median327.31
Q3331.48
95-th percentile347.98
Maximum827.26
Range693.93
Interquartile range (IQR)7.57

Descriptive statistics

Standard deviation14.2605451
Coefficient of variation (CV)0.04327440738
Kurtosis232.6310164
Mean329.5376174
Median Absolute Deviation (MAD)3.81
Skewness8.848607811
Sum17327747
Variance203.3631467
MonotonicityNot monotonic
2022-02-26T21:37:56.680039image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
326.671544
 
2.9%
328.891473
 
2.8%
324.441131
 
2.1%
331.11786
 
1.5%
322.22609
 
1.2%
320276
 
0.5%
333.33178
 
0.3%
351.11171
 
0.3%
348.89167
 
0.3%
346.67143
 
0.3%
Other values (3078)46104
87.6%
ValueCountFrequency (%)
133.3314
< 0.1%
133.962
 
< 0.1%
134.442
 
< 0.1%
134.852
 
< 0.1%
205.311
 
< 0.1%
221.611
 
< 0.1%
223.391
 
< 0.1%
282.461
 
< 0.1%
283.131
 
< 0.1%
287.571
 
< 0.1%
ValueCountFrequency (%)
827.262
< 0.1%
826.962
< 0.1%
695.221
< 0.1%
692.441
< 0.1%
658.521
< 0.1%
653.71
< 0.1%
653.371
< 0.1%
648.71
< 0.1%
644.31
< 0.1%
637.481
< 0.1%

evenTimeOnIce
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7637
Distinct (%)14.5%
Missing28
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean269.2122025
Minimum133.33
Maximum762.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size822.0 KiB
2022-02-26T21:37:56.820076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum133.33
5-th percentile218.131
Q1250.76
median270.96
Q3288.94
95-th percentile311.85
Maximum762.87
Range629.54
Interquartile range (IQR)38.18

Descriptive statistics

Standard deviation30.44647855
Coefficient of variation (CV)0.1130947196
Kurtosis9.030957688
Mean269.2122025
Median Absolute Deviation (MAD)18.89
Skewness0.4791046392
Sum14155716.03
Variance926.9880561
MonotonicityNot monotonic
2022-02-26T21:37:57.048350image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300371
 
0.7%
288.89343
 
0.7%
277.78268
 
0.5%
311.11239
 
0.5%
266.67215
 
0.4%
255.56105
 
0.2%
286.67103
 
0.2%
322.2299
 
0.2%
297.7881
 
0.2%
275.5673
 
0.1%
Other values (7627)50685
96.3%
ValueCountFrequency (%)
133.3314
< 0.1%
133.962
 
< 0.1%
134.442
 
< 0.1%
134.852
 
< 0.1%
137.071
 
< 0.1%
139.911
 
< 0.1%
140.741
 
< 0.1%
141.021
 
< 0.1%
143.351
 
< 0.1%
143.441
 
< 0.1%
ValueCountFrequency (%)
762.874
< 0.1%
574.441
 
< 0.1%
573.931
 
< 0.1%
572.931
 
< 0.1%
572.871
 
< 0.1%
567.331
 
< 0.1%
567.261
 
< 0.1%
563.761
 
< 0.1%
563.631
 
< 0.1%
556.31
 
< 0.1%

shortHandedTimeOnIce
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct3665
Distinct (%)7.0%
Missing28
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean26.7042623
Minimum0
Maximum115.48
Zeros751
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size822.0 KiB
2022-02-26T21:37:57.189272image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.8
Q117.78
median26.3
Q335.56
95-th percentile51.26
Maximum115.48
Range115.48
Interquartile range (IQR)17.78

Descriptive statistics

Standard deviation13.65571125
Coefficient of variation (CV)0.5113682265
Kurtosis0.530267905
Mean26.7042623
Median Absolute Deviation (MAD)8.67
Skewness0.6008795609
Sum1404163.52
Variance186.4784497
MonotonicityNot monotonic
2022-02-26T21:37:57.344698image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.784128
 
7.8%
26.672931
 
5.6%
8.892871
 
5.5%
35.561444
 
2.7%
0751
 
1.4%
44.44440
 
0.8%
26.69140
 
0.3%
24.44113
 
0.2%
26.5999
 
0.2%
53.3397
 
0.2%
Other values (3655)39568
75.2%
ValueCountFrequency (%)
0751
1.4%
0.074
 
< 0.1%
0.111
 
< 0.1%
0.151
 
< 0.1%
0.226
 
< 0.1%
0.37
 
< 0.1%
0.3710
 
< 0.1%
0.392
 
< 0.1%
0.4412
 
< 0.1%
0.5214
 
< 0.1%
ValueCountFrequency (%)
115.481
< 0.1%
112.221
< 0.1%
107.171
< 0.1%
98.041
< 0.1%
97.151
< 0.1%
97.091
< 0.1%
961
< 0.1%
95.911
< 0.1%
95.741
< 0.1%
94.281
< 0.1%

powerPlayTimeOnIce
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct4161
Distinct (%)7.9%
Missing28
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean33.62115039
Minimum0
Maximum146.3
Zeros752
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size822.0 KiB
2022-02-26T21:37:57.485101image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11.11
Q122.22
median33.17
Q344.44
95-th percentile64.81
Maximum146.3
Range146.3
Interquartile range (IQR)22.22

Descriptive statistics

Standard deviation17.22695003
Coefficient of variation (CV)0.5123843126
Kurtosis0.5208653759
Mean33.62115039
Median Absolute Deviation (MAD)10.95
Skewness0.6044131143
Sum1767867.33
Variance296.7678075
MonotonicityNot monotonic
2022-02-26T21:37:57.608423image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.224265
 
8.1%
33.333137
 
6.0%
11.112939
 
5.6%
44.441571
 
3.0%
0752
 
1.4%
55.56502
 
1.0%
31.11124
 
0.2%
66.67116
 
0.2%
33.24107
 
0.2%
38.8993
 
0.2%
Other values (4151)38976
74.1%
ValueCountFrequency (%)
0752
1.4%
0.094
 
< 0.1%
0.192
 
< 0.1%
0.286
 
< 0.1%
0.377
 
< 0.1%
0.469
 
< 0.1%
0.522
 
< 0.1%
0.5613
 
< 0.1%
0.6514
 
< 0.1%
0.7412
 
< 0.1%
ValueCountFrequency (%)
146.31
< 0.1%
130.981
< 0.1%
124.261
< 0.1%
122.941
< 0.1%
120.741
< 0.1%
119.811
< 0.1%
119.071
< 0.1%
118.041
< 0.1%
117.671
< 0.1%
116.221
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing35
Missing (%)0.1%
Memory size513.8 KiB
False
48498 
True
 
4077
(Missing)
 
35
ValueCountFrequency (%)
False48498
92.2%
True4077
 
7.7%
(Missing)35
 
0.1%
2022-02-26T21:37:57.702067image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

TeamName
Categorical

HIGH CORRELATION

Distinct33
Distinct (%)0.1%
Missing48
Missing (%)0.1%
Memory size822.0 KiB
Bruins
 
1822
Lightning
 
1804
St Louis Blues
 
1794
Sharks
 
1785
Washington Capitals
 
1785
Other values (28)
43572 

Length

Max length21
Median length15
Mean length14.00536509
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFlyers
2nd rowChicago Blackhawks
3rd rowAnaheim Ducks
4th rowBuffalo Sabres
5th rowColorado Avalanche

Common Values

ValueCountFrequency (%)
Bruins1822
 
3.5%
Lightning1804
 
3.4%
St Louis Blues1794
 
3.4%
Sharks1785
 
3.4%
Washington Capitals1785
 
3.4%
Pittsburgh Penguins1784
 
3.4%
Chicago Blackhawks1770
 
3.4%
NY Rangers1767
 
3.4%
Dallas Stars1761
 
3.3%
Nashville Predators1759
 
3.3%
Other values (23)34731
66.0%

Length

2022-02-26T21:37:57.785702image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ny3510
 
3.5%
bruins1822
 
1.8%
lightning1804
 
1.8%
louis1794
 
1.8%
blues1794
 
1.8%
st1794
 
1.8%
sharks1785
 
1.8%
washington1785
 
1.8%
capitals1785
 
1.8%
pittsburgh1784
 
1.8%
Other values (52)81823
80.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-02-26T21:37:47.952148image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-02-26T21:37:08.748239image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-02-26T21:37:27.283379image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-02-26T21:37:09.242817image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-26T21:37:11.670671image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-26T21:37:13.885368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-26T21:37:16.230934image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-02-26T21:37:20.834890image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-02-26T21:37:25.383525image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-26T21:37:27.756154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-02-26T21:37:12.940064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-26T21:37:15.264467image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-26T21:37:17.567121image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-26T21:37:19.892712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-26T21:37:22.249937image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-26T21:37:24.444603image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-26T21:37:26.793589image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-26T21:37:29.071115image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-26T21:37:31.440474image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-26T21:37:33.848329image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-26T21:37:36.168949image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-26T21:37:38.467575image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-26T21:37:40.786934image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-26T21:37:43.084822image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-26T21:37:45.445695image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-26T21:37:47.704765image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-26T21:37:50.253225image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-26T21:37:08.368391image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-26T21:37:10.822433image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-26T21:37:13.059688image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-26T21:37:15.377556image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-26T21:37:17.689139image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-26T21:37:20.007625image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-26T21:37:22.367530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-26T21:37:24.559614image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-26T21:37:26.910788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-26T21:37:29.327755image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-26T21:37:31.558033image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-26T21:37:33.969160image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-26T21:37:36.292143image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-26T21:37:38.581513image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-26T21:37:40.901906image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-26T21:37:43.206459image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-26T21:37:45.562921image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-26T21:37:47.826374image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2022-02-26T21:37:57.911046image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-26T21:37:58.152229image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-26T21:37:58.388376image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-26T21:37:58.600764image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-02-26T21:37:58.752447image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-26T21:37:50.511520image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-26T21:37:51.415977image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-02-26T21:37:51.863491image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-02-26T21:37:52.209353image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

gameIDhoawonsettledInheadCoachpimpowerPlayOpportunitiespowerPlayGoalsfaceOffWinPercentagestartRinkSideshotsgoalstakeawayshitsblockedShotsgiveawaysmissedShotspenaltieswonFaceoffsteamIDtimeOnIceevenTimeOnIceshortHandedTimeOnIcepowerPlayTimeOnIcegoalieReplacementTeamName
02016020045awayFalseREGDave Hakstol2.001.330.6750.9left8.00.01.014.03.06.04.01.010.04.0330.28280.4618.5231.30YesFlyers
12016020045homeTrueREGJoel Quenneville2.671.000.6749.1left8.03.03.05.03.07.00.01.012.016.0327.11278.9325.0423.15NoChicago Blackhawks
22017020812awayTrueOTRandy Carlyle2.001.000.3343.8right11.00.00.04.06.02.06.01.011.024.0334.07293.209.4831.39NoAnaheim Ducks
32017020812homeFalseOTPhil Housley2.670.670.3356.2right12.01.02.04.08.00.03.03.09.07.0333.07296.1125.1111.85NoBuffalo Sabres
42015020314awayTrueREGPatrick Roy3.001.000.3345.7left9.00.03.04.07.07.03.02.07.021.0328.89281.5717.7829.54NoColorado Avalanche
52015020314homeFalseREGPaul Maurice3.670.670.0054.3left8.01.02.05.01.05.08.01.011.052.0328.91283.0623.6322.22NoWinnipeg Jets
62015020849awayFalseREGPaul Maurice3.331.330.0031.4left4.00.05.012.03.02.03.01.05.052.0324.78254.3535.7034.72NoWinnipeg Jets
72015020849homeTrueREGBill Peters2.671.670.6768.6left8.01.05.05.04.02.03.03.012.012.0324.48252.0727.7844.63NoCarolina Hurricanes
82017020586awayFalseREGGlen Gulutzan6.331.000.0054.7right5.00.02.09.03.04.02.05.07.020.0324.72261.7638.1524.81NoCalgary Flames
92017020586homeTrueREGRandy Carlyle4.332.000.3345.3right19.01.04.06.03.05.06.03.011.024.0326.54259.3119.8547.37NoAnaheim Ducks

Last rows

gameIDhoawonsettledInheadCoachpimpowerPlayOpportunitiespowerPlayGoalsfaceOffWinPercentagestartRinkSideshotsgoalstakeawayshitsblockedShotsgiveawaysmissedShotspenaltieswonFaceoffsteamIDtimeOnIceevenTimeOnIceshortHandedTimeOnIcepowerPlayTimeOnIcegoalieReplacementTeamName
526002018030415awayTrueREGCraig Berube2.000.330.0059.4right16.00.02.036.016.06.00.02.024.019.0326.67288.8926.6711.11NoSt Louis Blues
526012018030415homeFalseREGBruce Cassidy0.671.000.0040.6right34.00.010.046.012.00.04.02.08.06.0332.56290.338.8933.33NoBruins
526022018030416awayTrueREGBruce Cassidy3.331.330.3341.3right22.02.02.020.016.02.02.04.014.06.0324.04270.0035.5618.48NoBruins
526032018030416homeFalseREGCraig Berube6.671.330.0058.7right18.00.012.018.08.08.010.04.020.019.0330.94272.0914.4144.44NoSt Louis Blues
526042018030416awayTrueREGBruce Cassidy3.331.330.3341.3right22.02.02.020.016.02.02.04.014.06.0324.04270.0035.5618.48NoBruins
526052018030416homeFalseREGCraig Berube6.671.330.0058.7right18.00.012.018.08.08.010.04.020.019.0330.94272.0914.4144.44NoSt Louis Blues
526062018030417awayTrueREGCraig Berube0.670.000.0049.0right4.04.04.028.018.00.04.02.014.019.0331.11322.228.890.00NoSt Louis Blues
526072018030417homeFalseREGBruce Cassidy0.000.330.0051.0right24.00.010.022.04.010.02.00.022.06.0337.15326.040.0011.11NoBruins
526082018030417awayTrueREGCraig Berube0.670.000.0049.0right4.04.04.028.018.00.04.02.014.019.0331.11322.228.890.00NoSt Louis Blues
526092018030417homeFalseREGBruce Cassidy0.000.330.0051.0right24.00.010.022.04.010.02.00.022.06.0337.15326.040.0011.11NoBruins

Duplicate rows

Most frequently occurring

gameIDhoawonsettledInheadCoachpimpowerPlayOpportunitiespowerPlayGoalsfaceOffWinPercentagestartRinkSideshotsgoalstakeawayshitsblockedShotsgiveawaysmissedShotspenaltieswonFaceoffsteamIDtimeOnIceevenTimeOnIceshortHandedTimeOnIcepowerPlayTimeOnIcegoalieReplacementTeamName# duplicates
02018020001awayFalseOTClaude Julien2.001.330.3341.3left16.02.04.028.08.02.06.02.010.08.0330.15272.3322.0735.74NoMontreal Canadiens2
12018020001homeTrueOTMike Babcock2.671.000.3358.7left16.02.02.014.012.014.014.00.022.010.0328.52272.3328.5927.59NoToronto Maple Leafs2
22018020002awayFalseREGBruce Cassidy10.670.670.0068.3left18.00.02.030.08.02.014.02.036.06.0324.59269.0733.3022.22YesBruins2
32018020002homeTrueREGTodd Reirden4.672.001.3331.7left22.04.010.014.012.010.010.02.08.015.0328.96269.1517.7842.04NoWashington Capitals2
42018020003awayFalseREGBill Peters2.332.330.0057.6left18.00.00.018.04.04.014.02.022.020.0332.65245.988.8977.78NoCalgary Flames2
52018020003homeTrueREGTravis Green6.330.330.0042.4left18.02.08.020.06.08.00.06.026.023.0317.78244.4462.2211.11NoVancouver Canucks2
62018020004awayTrueREGRandy Carlyle2.001.000.6748.3right8.02.08.018.010.04.02.04.022.024.0326.67297.8718.1510.65NoAnaheim Ducks2
72018020004homeFalseREGPeter DeBoer2.001.000.0051.7right20.02.06.022.012.06.012.02.024.028.0330.30299.078.5422.69NoSharks2
82018020005awayTrueREGBruce Cassidy2.000.330.3343.3right18.04.00.012.02.08.010.00.020.06.0330.39309.7211.788.89NoBruins2
92018020005homeFalseREGPhil Housley1.330.670.0056.7right12.00.00.010.04.012.010.02.026.07.0336.94314.617.1115.22NoBuffalo Sabres2